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Even when starting with a very poor initial guess, the iterative configuration interaction (iCI) approach can converge from above to full CI very quickly by constructing and diagonalizing a small Hamiltonian matrix at each…

Chemical Physics · Physics 2020-01-07 Ning Zhang , Wenjian Liu , Mark R. Hoffmann

Practical applications of fragment embedding and closely related local correlation methods critically depend on a judicious choice of a low-level theory to define the local embedding subspace and to capture long-range electrostatic and…

Chemical Physics · Physics 2026-05-14 Ruiheng Song , Xiliang Gong , Aamy Bakry , Hong-Zhou Ye

The charge delocalization error, besides nondynamic correlation, has been a major challenge to density functional theory. Contemporary functionals undershoot the dissociation of symmetric charged dimers A2+, a simple but stringent test,…

Chemical Physics · Physics 2021-07-26 Emil Proynov , Jing Kong

A stochastic resolution of identity approach (sRI) is applied to the second-order coupled cluster singles and doubles (CC2) model to calculate the ground-state energy. Utilizing a set of stochastic orbitals to optimize the expensive tensor…

Chemical Physics · Physics 2022-10-25 Chongxiao Zhao , Wenjie Dou

A new class of orbital-dependent exchange-correlation (xc) potentials for applications in noncollinear spin-density-functional theory is developed. Starting from the optimized effective potential (OEP) formalism for the exact exchange…

Mesoscale and Nanoscale Physics · Physics 2018-08-01 Carsten A. Ullrich

Spin manipulation in coupled quantum dots is of interest for quantum information applications. Control of the exchange interaction between electrons and holes via an applied electric field may provide a promising technique for such spin…

A new state specific correlation correction to configuration interaction singles (CIS) excitation energies is preseted using coupled cluster perturbation theory (CCPT). General expressions for CIS-CCPT are derived and expanded explicitly to…

Chemical Physics · Physics 2015-06-19 Jason N. Byrd , Victor F. Lotrich , Rodney J. Bartlett

A one-dimensional diagonal tight binding electronic system with correlated disorder is investigated. The correlation of the random potential is exponentially decaying with distance and its correlation length diverges as the concentration of…

Disordered Systems and Neural Networks · Physics 2009-11-11 I Avgin

Coupled cluster theory is one of the most accurate electronic structure methods for predicting ground and excited state chemistry. However, the presence of numerical artifacts at electronic degeneracies, such as complex energies, has made…

Chemical Physics · Physics 2024-05-16 Eirik F. Kjønstad , Sara Angelico , Henrik Koch

Chiral-induced spin selectivity (CISS) enables spin selectivity of charge carriers in chiral molecular systems without magnetic materials. While spin selectivity has been widely investigated, its quantum coherence has not yet been explored.…

Quantum Physics · Physics 2026-01-09 Hanna T. Fridman , Rotem Malkinson , Amir Hen , Shira Yochelis , Yossi Paltiel , Nir Bar-gill

The accurate determination of the preferred ${\rm Si}_{12}{\rm C}_{12}$ isomer is important to guide experimental efforts directed towards synthesizing SiC nano-wires and related polymer structures which are anticipated to be highly…

Excited-state molecular dynamics (ESMD) simulations near conical intersections (CIs) pose significant challenges when using machine learning potentials (MLPs). Although MLPs have gained recognition for their integration into mixed…

Chemical Physics · Physics 2025-01-17 Sung Wook Moon , Soohaeng Yoo Willow , Tae Hyeon Park , Seung Kyu Min , Chang Woo Myung

Model Predictive Control (MPC) offers rigorous safety and performance guarantees but is computationally intensive. Approximate MPC (AMPC) aims to circumvent this drawback by learning a computationally cheaper surrogate policy. Common…

Systems and Control · Electrical Eng. & Systems 2025-11-19 Elias Milios , Kim P. Wabersich , Felix Berkel , Felix Gruber , Melanie N. Zeilinger

The combination of neural network potential (NNP) with molecular simulations plays an important role in an efficient and thorough understanding of a molecular system's potential energy surface (PES). However, grasping the interplay between…

Computational Physics · Physics 2021-10-28 Ji Woong Yu , Min Young Ha , Bumjoon Seo , Won Bo Lee

We identify the dominant computational cost within the recently introduced stochastic and internally contracted FCIQMC-NEVPT2 method for large active space sizes. This arises from the contribution to the four-body intermediates arising from…

Chemical Physics · Physics 2020-10-05 James J. Halson , Robert J. Anderson , George H. Booth

We revisit the dissipative approach to producing and stabilizing spin-squeezed states of an ensemble of $N$ two-level systems, providing a detailed analysis of two surprising yet generic features of such protocols. The first is a…

Quantum Physics · Physics 2022-02-08 Peter Groszkowski , Martin Koppenhöfer , Hoi-Kwan Lau , A. A. Clerk

An accurate description of electron correlation is one of the most challenging problems in quantum chemistry. The exact electron correlation can be obtained by means of full configuration interaction (FCI). A simple strategy for…

Chemical Physics · Physics 2021-05-14 Jae Woo Park

While the usual goal in Monte Carlo (MC) simulations of Ising models is the efficient generation of spin configurations with Boltzmann probabilities, the inverse problem is to determine the coupling constants from a given set of spin…

Disordered Systems and Neural Networks · Physics 2017-05-24 Joseph Albert , Robert H. Swendsen

We introduce a size-consistent and orbital-invariant formalism for constructing correlation functionals based on the adiabatic connection for density functional theory (DFT). By constructing correlation energy matrices for the weak and…

Chemical Physics · Physics 2025-11-05 Kyle Bystrom , Timothy C. Berkelbach

Direct methods are widely used for alignment of models to images, due to their accuracy, since they minimize errors in the domain of measurement noise. They have leveraged least squares minimizations, for simple, efficient, variational…

Computer Vision and Pattern Recognition · Computer Science 2022-03-31 Oliver J. Woodford